SummaryHumans learn best from feedback–we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the technologyDeep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error.About the bookDeep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym.What’s insideBuilding and training DRL networksThe most popular DRL algorithms for learning and problem solvingEvolutionary algorithms for curiosity and multi-agent learningAll examples available as Jupyter NotebooksAbout the readerFor readers with intermediate skills in Python and deep learning.About the authorAlexander Zai is a machine learning engineer at Amazon AI. Brandon Brown is a machine learning and data analysis blogger.Table of ContentsPART 1 – FOUNDATIONS1. What is reinforcement learning?2. Modeling reinforcement learning Markov decision processes3. Predicting the best states and Deep Q-networks4. Learning to pick the best Policy gradient methods5. Tackling more complex problems with actor-critic methodsPART 2 – ABOVE AND BEYOND6. Alternative optimization Evolutionary algorithms7. Distributional Getting the full story8.Curiosity-driven exploration9. Multi-agent reinforcement learning10. Interpretable reinforcement Attention and relational models11. In A review and roadmap

Deep Reinforcement Learning in Action PDF

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